Overview

Dataset statistics

Number of variables11
Number of observations193573
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.2 MiB
Average record size in memory88.0 B

Variable types

Numeric8
Categorical3

Alerts

id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2024-06-03 19:31:53.838698
Analysis finished2024-06-03 19:32:01.144510
Duration7.31 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct193573
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96786
Minimum0
Maximum193572
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:01.211371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9678.6
Q148393
median96786
Q3145179
95-th percentile183893.4
Maximum193572
Range193572
Interquartile range (IQR)96786

Descriptive statistics

Standard deviation55879.856
Coefficient of variation (CV)0.57735474
Kurtosis-1.2
Mean96786
Median Absolute Deviation (MAD)48393
Skewness0
Sum1.8735156 × 1010
Variance3.1225583 × 109
MonotonicityStrictly increasing
2024-06-04T01:02:01.301366image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
129053 1
 
< 0.1%
129044 1
 
< 0.1%
129045 1
 
< 0.1%
129046 1
 
< 0.1%
129047 1
 
< 0.1%
129048 1
 
< 0.1%
129049 1
 
< 0.1%
129050 1
 
< 0.1%
129051 1
 
< 0.1%
Other values (193563) 193563
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
193572 1
< 0.1%
193571 1
< 0.1%
193570 1
< 0.1%
193569 1
< 0.1%
193568 1
< 0.1%
193567 1
< 0.1%
193566 1
< 0.1%
193565 1
< 0.1%
193564 1
< 0.1%
193563 1
< 0.1%

carat
Real number (ℝ)

Distinct248
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79068785
Minimum0.2
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:01.381176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.4
median0.7
Q31.03
95-th percentile1.65
Maximum3.5
Range3.3
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation0.46268774
Coefficient of variation (CV)0.58517117
Kurtosis0.53739769
Mean0.79068785
Median Absolute Deviation (MAD)0.32
Skewness0.99513461
Sum153055.82
Variance0.21407994
MonotonicityNot monotonic
2024-06-04T01:02:01.461399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 10758
 
5.6%
1.01 10103
 
5.2%
0.31 9538
 
4.9%
0.7 7958
 
4.1%
0.32 7548
 
3.9%
0.9 6253
 
3.2%
0.41 5852
 
3.0%
0.71 5367
 
2.8%
1 5328
 
2.8%
0.4 4802
 
2.5%
Other values (238) 120066
62.0%
ValueCountFrequency (%)
0.2 30
 
< 0.1%
0.21 17
 
< 0.1%
0.22 3
 
< 0.1%
0.23 889
0.5%
0.24 809
0.4%
0.25 611
0.3%
0.26 722
0.4%
0.27 650
0.3%
0.28 466
0.2%
0.29 354
 
0.2%
ValueCountFrequency (%)
3.5 1
 
< 0.1%
3.4 1
 
< 0.1%
3.04 3
< 0.1%
3.01 7
< 0.1%
3 5
< 0.1%
2.74 3
< 0.1%
2.72 1
 
< 0.1%
2.71 1
 
< 0.1%
2.7 2
 
< 0.1%
2.66 3
< 0.1%

cut
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Ideal
92454 
Premium
49910 
Very Good
37566 
Good
11622 
Fair
 
2021

Length

Max length9
Median length7
Mean length6.2214565
Min length4

Characters and Unicode

Total characters1204306
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium
2nd rowVery Good
3rd rowIdeal
4th rowIdeal
5th rowPremium

Common Values

ValueCountFrequency (%)
Ideal 92454
47.8%
Premium 49910
25.8%
Very Good 37566
19.4%
Good 11622
 
6.0%
Fair 2021
 
1.0%

Length

2024-06-04T01:02:01.538167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T01:02:01.608530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ideal 92454
40.0%
premium 49910
21.6%
good 49188
21.3%
very 37566
16.3%
fair 2021
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 179930
14.9%
d 141642
11.8%
m 99820
8.3%
o 98376
8.2%
a 94475
7.8%
I 92454
7.7%
l 92454
7.7%
r 89497
7.4%
i 51931
 
4.3%
P 49910
 
4.1%
Other values (6) 213817
17.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 935601
77.7%
Uppercase Letter 231139
 
19.2%
Space Separator 37566
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 179930
19.2%
d 141642
15.1%
m 99820
10.7%
o 98376
10.5%
a 94475
10.1%
l 92454
9.9%
r 89497
9.6%
i 51931
 
5.6%
u 49910
 
5.3%
y 37566
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
I 92454
40.0%
P 49910
21.6%
G 49188
21.3%
V 37566
16.3%
F 2021
 
0.9%
Space Separator
ValueCountFrequency (%)
37566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1166740
96.9%
Common 37566
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 179930
15.4%
d 141642
12.1%
m 99820
8.6%
o 98376
8.4%
a 94475
8.1%
I 92454
7.9%
l 92454
7.9%
r 89497
7.7%
i 51931
 
4.5%
P 49910
 
4.3%
Other values (5) 176251
15.1%
Common
ValueCountFrequency (%)
37566
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1204306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 179930
14.9%
d 141642
11.8%
m 99820
8.3%
o 98376
8.2%
a 94475
7.8%
I 92454
7.7%
l 92454
7.7%
r 89497
7.4%
i 51931
 
4.3%
P 49910
 
4.1%
Other values (6) 213817
17.8%

color
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
G
44391 
E
35869 
F
34258 
H
30799 
D
24286 
Other values (2)
23970 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters193573
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowJ
3rd rowG
4th rowG
5th rowG

Common Values

ValueCountFrequency (%)
G 44391
22.9%
E 35869
18.5%
F 34258
17.7%
H 30799
15.9%
D 24286
12.5%
I 17514
 
9.0%
J 6456
 
3.3%

Length

2024-06-04T01:02:01.685955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T01:02:01.753787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
g 44391
22.9%
e 35869
18.5%
f 34258
17.7%
h 30799
15.9%
d 24286
12.5%
i 17514
 
9.0%
j 6456
 
3.3%

Most occurring characters

ValueCountFrequency (%)
G 44391
22.9%
E 35869
18.5%
F 34258
17.7%
H 30799
15.9%
D 24286
12.5%
I 17514
 
9.0%
J 6456
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 193573
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 44391
22.9%
E 35869
18.5%
F 34258
17.7%
H 30799
15.9%
D 24286
12.5%
I 17514
 
9.0%
J 6456
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 193573
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 44391
22.9%
E 35869
18.5%
F 34258
17.7%
H 30799
15.9%
D 24286
12.5%
I 17514
 
9.0%
J 6456
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 44391
22.9%
E 35869
18.5%
F 34258
17.7%
H 30799
15.9%
D 24286
12.5%
I 17514
 
9.0%
J 6456
 
3.3%

clarity
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
SI1
53272 
VS2
48027 
VS1
30669 
SI2
30484 
VVS2
15762 
Other values (3)
15359 

Length

Max length4
Median length3
Mean length3.1118906
Min length2

Characters and Unicode

Total characters602378
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVS2
2nd rowSI2
3rd rowVS1
4th rowVS1
5th rowVS2

Common Values

ValueCountFrequency (%)
SI1 53272
27.5%
VS2 48027
24.8%
VS1 30669
15.8%
SI2 30484
15.7%
VVS2 15762
 
8.1%
VVS1 10628
 
5.5%
IF 4219
 
2.2%
I1 512
 
0.3%

Length

2024-06-04T01:02:01.845706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-04T01:02:01.919400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
si1 53272
27.5%
vs2 48027
24.8%
vs1 30669
15.8%
si2 30484
15.7%
vvs2 15762
 
8.1%
vvs1 10628
 
5.5%
if 4219
 
2.2%
i1 512
 
0.3%

Most occurring characters

ValueCountFrequency (%)
S 188842
31.3%
V 131476
21.8%
1 95081
15.8%
2 94273
15.7%
I 88487
14.7%
F 4219
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 413024
68.6%
Decimal Number 189354
31.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 188842
45.7%
V 131476
31.8%
I 88487
21.4%
F 4219
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 95081
50.2%
2 94273
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 413024
68.6%
Common 189354
31.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 188842
45.7%
V 131476
31.8%
I 88487
21.4%
F 4219
 
1.0%
Common
ValueCountFrequency (%)
1 95081
50.2%
2 94273
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 602378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 188842
31.3%
V 131476
21.8%
1 95081
15.8%
2 94273
15.7%
I 88487
14.7%
F 4219
 
0.7%

depth
Real number (ℝ)

Distinct153
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.820574
Minimum52.1
Maximum71.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:02.015332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum52.1
5-th percentile59.9
Q161.3
median61.9
Q362.4
95-th percentile63.5
Maximum71.6
Range19.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.0817044
Coefficient of variation (CV)0.017497482
Kurtosis2.4770411
Mean61.820574
Median Absolute Deviation (MAD)0.6
Skewness-0.27638236
Sum11966794
Variance1.1700843
MonotonicityNot monotonic
2024-06-04T01:02:02.104869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.9 10781
 
5.6%
62 10150
 
5.2%
61.8 9270
 
4.8%
62.1 8866
 
4.6%
61.6 8534
 
4.4%
62.2 8345
 
4.3%
62.3 7987
 
4.1%
61.7 7970
 
4.1%
62.4 7030
 
3.6%
61.5 6554
 
3.4%
Other values (143) 108086
55.8%
ValueCountFrequency (%)
52.1 1
 
< 0.1%
52.2 1
 
< 0.1%
52.7 1
 
< 0.1%
53.1 2
 
< 0.1%
53.2 2
 
< 0.1%
54.7 1
 
< 0.1%
54.9 2
 
< 0.1%
55 1
 
< 0.1%
55.1 1
 
< 0.1%
55.2 5
< 0.1%
ValueCountFrequency (%)
71.6 2
< 0.1%
70 1
 
< 0.1%
69.9 1
 
< 0.1%
69.6 1
 
< 0.1%
69.5 3
< 0.1%
69.4 1
 
< 0.1%
69.2 2
< 0.1%
69.1 1
 
< 0.1%
69 1
 
< 0.1%
68.9 1
 
< 0.1%

table
Real number (ℝ)

Distinct108
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.227675
Minimum49
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:02.200416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile54.2
Q156
median57
Q358
95-th percentile61
Maximum79
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9188443
Coefficient of variation (CV)0.033530006
Kurtosis0.81018001
Mean57.227675
Median Absolute Deviation (MAD)1
Skewness0.61906223
Sum11077733
Variance3.6819634
MonotonicityNot monotonic
2024-06-04T01:02:02.286369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 42194
21.8%
57 37827
19.5%
58 32045
16.6%
55 24429
12.6%
59 23784
12.3%
60 12584
 
6.5%
54 8281
 
4.3%
61 6002
 
3.1%
62 2545
 
1.3%
53 1069
 
0.6%
Other values (98) 2813
 
1.5%
ValueCountFrequency (%)
49 1
 
< 0.1%
51 6
 
< 0.1%
52 50
 
< 0.1%
53 1069
0.6%
53.1 1
 
< 0.1%
53.2 8
 
< 0.1%
53.3 17
 
< 0.1%
53.4 7
 
< 0.1%
53.5 10
 
< 0.1%
53.6 40
 
< 0.1%
ValueCountFrequency (%)
79 1
 
< 0.1%
76 1
 
< 0.1%
70 5
 
< 0.1%
69 10
 
< 0.1%
68 20
 
< 0.1%
67 32
 
< 0.1%
66 114
 
0.1%
65 157
0.1%
64.4 1
 
< 0.1%
64 376
0.2%

x
Real number (ℝ)

Distinct522
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7153121
Minimum0
Maximum9.65
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:02.378500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.29
Q14.7
median5.7
Q36.51
95-th percentile7.58
Maximum9.65
Range9.65
Interquartile range (IQR)1.81

Descriptive statistics

Standard deviation1.1094222
Coefficient of variation (CV)0.19411401
Kurtosis-0.80100603
Mean5.7153121
Median Absolute Deviation (MAD)0.92
Skewness0.36104978
Sum1106330.1
Variance1.2308175
MonotonicityNot monotonic
2024-06-04T01:02:02.468400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.32 2094
 
1.1%
4.34 2010
 
1.0%
4.38 1986
 
1.0%
4.37 1938
 
1.0%
4.33 1812
 
0.9%
4.31 1761
 
0.9%
4.35 1685
 
0.9%
4.41 1644
 
0.8%
4.36 1568
 
0.8%
4.3 1558
 
0.8%
Other values (512) 175517
90.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
3.75 2
 
< 0.1%
3.77 7
< 0.1%
3.78 5
< 0.1%
3.79 4
< 0.1%
3.81 7
< 0.1%
3.82 6
< 0.1%
3.84 9
< 0.1%
3.85 2
 
< 0.1%
3.86 9
< 0.1%
ValueCountFrequency (%)
9.65 1
 
< 0.1%
9.51 1
 
< 0.1%
9.46 1
 
< 0.1%
9.43 1
 
< 0.1%
9.42 4
< 0.1%
9.36 3
< 0.1%
9.35 1
 
< 0.1%
9.3 1
 
< 0.1%
9.24 1
 
< 0.1%
9.1 1
 
< 0.1%

y
Real number (ℝ)

Distinct521
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7200944
Minimum0
Maximum10.01
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:02.557409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.31
Q14.71
median5.72
Q36.51
95-th percentile7.58
Maximum10.01
Range10.01
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1023335
Coefficient of variation (CV)0.19271246
Kurtosis-0.81066804
Mean5.7200944
Median Absolute Deviation (MAD)0.92
Skewness0.35675813
Sum1107255.8
Variance1.2151391
MonotonicityNot monotonic
2024-06-04T01:02:02.645997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.38 2163
 
1.1%
4.35 2088
 
1.1%
4.34 1986
 
1.0%
4.37 1911
 
1.0%
4.31 1854
 
1.0%
4.39 1719
 
0.9%
4.33 1711
 
0.9%
4.36 1625
 
0.8%
4.32 1618
 
0.8%
4.41 1593
 
0.8%
Other values (511) 175305
90.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
3.71 1
 
< 0.1%
3.72 7
< 0.1%
3.73 1
 
< 0.1%
3.75 2
 
< 0.1%
3.77 4
< 0.1%
3.78 9
< 0.1%
3.79 2
 
< 0.1%
3.8 1
 
< 0.1%
3.81 5
< 0.1%
ValueCountFrequency (%)
10.01 1
< 0.1%
9.59 1
< 0.1%
9.46 1
< 0.1%
9.36 1
< 0.1%
9.34 1
< 0.1%
9.31 1
< 0.1%
9.3 2
< 0.1%
9.26 2
< 0.1%
9.24 2
< 0.1%
9.14 1
< 0.1%

z
Real number (ℝ)

Distinct349
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5342463
Minimum0
Maximum31.3
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:02.732617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.66
Q12.9
median3.53
Q34.03
95-th percentile4.684
Maximum31.3
Range31.3
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.68892211
Coefficient of variation (CV)0.19492759
Kurtosis12.818313
Mean3.5342463
Median Absolute Deviation (MAD)0.57
Skewness0.68567148
Sum684134.67
Variance0.47461367
MonotonicityNot monotonic
2024-06-04T01:02:02.817617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.69 3523
 
1.8%
2.7 3305
 
1.7%
2.68 3289
 
1.7%
2.71 3087
 
1.6%
2.72 2887
 
1.5%
2.73 2747
 
1.4%
2.67 2628
 
1.4%
3.99 2542
 
1.3%
4.02 2430
 
1.3%
4.01 2384
 
1.2%
Other values (339) 164751
85.1%
ValueCountFrequency (%)
0 10
< 0.1%
1.05 1
 
< 0.1%
2.24 2
 
< 0.1%
2.26 1
 
< 0.1%
2.27 2
 
< 0.1%
2.28 1
 
< 0.1%
2.3 4
 
< 0.1%
2.31 9
< 0.1%
2.32 5
< 0.1%
2.33 7
< 0.1%
ValueCountFrequency (%)
31.3 1
< 0.1%
8.4 1
< 0.1%
8.35 1
< 0.1%
8.18 1
< 0.1%
6.03 1
< 0.1%
5.75 1
< 0.1%
5.73 1
< 0.1%
5.69 1
< 0.1%
5.67 2
< 0.1%
5.65 1
< 0.1%

price
Real number (ℝ)

Distinct8738
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3969.1554
Minimum326
Maximum18818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-06-04T01:02:02.907617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum326
5-th percentile544
Q1951
median2401
Q35408
95-th percentile13298
Maximum18818
Range18492
Interquartile range (IQR)4457

Descriptive statistics

Standard deviation4034.3741
Coefficient of variation (CV)1.0164314
Kurtosis2.106914
Mean3969.1554
Median Absolute Deviation (MAD)1678
Skewness1.6055812
Sum7.6832132 × 108
Variance16276175
MonotonicityNot monotonic
2024-06-04T01:02:02.999690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
544 542
 
0.3%
605 464
 
0.2%
789 454
 
0.2%
828 438
 
0.2%
776 437
 
0.2%
802 435
 
0.2%
552 427
 
0.2%
561 416
 
0.2%
625 414
 
0.2%
596 401
 
0.2%
Other values (8728) 189145
97.7%
ValueCountFrequency (%)
326 15
< 0.1%
335 11
 
< 0.1%
336 11
 
< 0.1%
337 5
 
< 0.1%
338 11
 
< 0.1%
344 5
 
< 0.1%
345 15
< 0.1%
348 5
 
< 0.1%
351 25
< 0.1%
357 31
< 0.1%
ValueCountFrequency (%)
18818 6
 
< 0.1%
18804 9
< 0.1%
18795 10
< 0.1%
18791 15
< 0.1%
18787 5
 
< 0.1%
18780 6
 
< 0.1%
18766 12
< 0.1%
18760 6
 
< 0.1%
18759 9
< 0.1%
18757 8
< 0.1%

Interactions

2024-06-04T01:01:59.970653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:55.351279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.110149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.723497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.453595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.061091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.651431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.267859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:02:00.054089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:55.552801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.188572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.911666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.534349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.138817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.729630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.366728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:02:00.132855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:55.631075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.265203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.985247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.608679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.215306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.802007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.457397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:02:00.215580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:55.720154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.347607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.067826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.688534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.292633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.878891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.557284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:02:00.292789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:55.799312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.423120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.145826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.762884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.365371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.950387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.647171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:02:00.500170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:55.876731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.497396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.221002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.833416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.432228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.018462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.747291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:02:00.579144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:55.950930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.568201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.296853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.905298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.502016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.091071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.822074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:02:00.660285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.028768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:56.644731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.373070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:57.980699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:58.573330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.175050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-04T01:01:59.895345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-06-04T01:02:00.763671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-04T01:02:00.926168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idcaratcutcolorclaritydepthtablexyzprice
001.52PremiumFVS262.258.07.277.334.5513619
112.03Very GoodJSI262.058.08.068.125.0513387
220.70IdealGVS161.257.05.695.733.502772
330.32IdealGVS161.656.04.384.412.71666
441.70PremiumGVS262.659.07.657.614.7714453
551.51Very GoodJSI162.858.07.347.294.597506
660.74IdealEVS261.857.05.765.793.573229
771.34PremiumGSI262.557.07.007.054.386224
880.30IdealFIF62.056.04.354.372.70886
990.30GoodJVS163.657.04.264.282.72421
idcaratcutcolorclaritydepthtablexyzprice
1935631935630.28Very GoodESI161.255.04.234.262.60484
1935641935640.90GoodDSI163.257.06.116.143.884919
1935651935650.31PremiumDSI162.658.04.324.292.69732
1935661935661.05IdealGVS162.156.06.546.514.067397
1935671935670.58IdealEVS261.857.05.335.363.311872
1935681935680.31IdealDVVS261.156.04.354.392.671130
1935691935690.70PremiumGVVS260.358.05.755.773.472874
1935701935700.73Very GoodFSI163.157.05.725.753.623036
1935711935710.34Very GoodDSI162.955.04.454.492.81681
1935721935720.71GoodESI260.864.05.735.713.482258